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Decodificación de Movimientos Individuales de los Dedos y Agarre a Partir de Señales Mioeléctricas de Baja Densidad

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Decodificación de Movimientos Individuales de los Dedos y Agarre a Partir de Señales Mioeléctricas de Baja Densidad

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dc.contributor.author Villarejo Mayor, John J. es_ES
dc.contributor.author Mamede Costa, Regina es_ES
dc.contributor.author Frizera Neto, Anselmo es_ES
dc.contributor.author Freire Bastos, Teodiano es_ES
dc.date.accessioned 2020-05-18T08:18:52Z
dc.date.available 2020-05-18T08:18:52Z
dc.date.issued 2017-04-03
dc.identifier.issn 1697-7912
dc.identifier.uri http://hdl.handle.net/10251/143518
dc.description.abstract [ES] Uno de los principales retos en el diseño de prótesis de mano es poder establecer un control intuitivo que reduzca el esfuerzo del usuario durante su entrenamiento. Este trabajo presenta un esquema para identificar tareas de motricidad fina de la mano, agrupadas en movimientos de los dedos individuales y gestos para el agarre de objetos el cual se ha validado con sujetos amputados. Se han comparado diferentes métodos de selección de características y clasificadores para el reconocimiento de patrones mioeléctricos, utilizando cuatro electrodos superficiales. Las características de las señales en el dominio del tiempo y la frecuencia se han combinado con métodos no lineales basados en análisis de fractales, mostrando una diferencia significativa en comparación con los métodos expuestos en la literatura para clasificar tareas de fuerza. Los resultados con amputados mostraron una exactitud de hasta 99,4% en los movimientos individuales de los dedos, superior a la obtenida con los gestos de agarre, de hasta 93,3%. El sistema ha obtenido una tasa de acierto promedio de 86,3% utilizando máquinas de soporte vectorial (SVM), seguido muy de cerca por K-vecinos más cercanos (KNN) con 83,4%. Sin embargo, KNN ha obtenido un mejor rendimiento global, debido a que es más rápido que SVM, lo que representa una ventaja para aplicaciones en tiempo real. El método aquí propuesto ofrece una mayor funcionalidad en el control de prótesis de mano, lo que mejoraría su aceptación por parte de los amputados. es_ES
dc.description.abstract [EN] Intuitive prosthesis control is one of the most important challenges in order to reduce the user effort in learning to use an artificial hand. This work presents the development of a myoelectric pattern recognition system for myoelectric weak signals able to discriminate dexterous hand movements using a reduced number of electrodes. The system was evaluated in six forearm amputees and the results were compared with the performance of able-bodied subjects. Different methods were analyzed to classify individual fingers flexion, hand gestures and different grasps using four electrodes and considering the low level of muscle contraction in these tasks. Multiple features of sEMG signals were also analyzed considering traditional magnitude-based features and fractal analysis. Statistical significance was computed for all the methods using different set of features, for both groups of subjects (able-bodied and amputees). For amputees, results showed accuracy up to 99.4% for individual finger movements, higher than the achieved by grasp movements, up to 93.3%. Best performance was achieved using support vector machine (SVM), followed very closely by K-nearest neighbors (KNN). However, KNN produces a better global performance because it is faster than SVM, which implies an advantage for real-time applications. The results show that the method here proposed is suitable for accurately controlling dexterous prosthetic hands, providing more functionality and better acceptance for amputees. es_ES
dc.description.sponsorship Este trabajo ha sido patrocinado por CAPES y FAPES/Brasil (Proyecto Número 007/2014: Use of Robotics and Assistive Technology for Children and Adults with Disabilities). es_ES
dc.language Español es_ES
dc.publisher Universitat Politècnica de València es_ES
dc.relation.ispartof Revista Iberoamericana de Automática e Informática industrial es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Señales electromiográficas es_ES
dc.subject Prótesis de miembro superior es_ES
dc.subject Reconocimiento de patrones es_ES
dc.subject Tareas de destreza de la mano es_ES
dc.subject Myoelectric signals es_ES
dc.subject Upper-limb prosthesis es_ES
dc.subject Superficial electromyography low density es_ES
dc.subject Dexterous hand gestures es_ES
dc.subject Pattern recognition es_ES
dc.title Decodificación de Movimientos Individuales de los Dedos y Agarre a Partir de Señales Mioeléctricas de Baja Densidad es_ES
dc.title.alternative Decoding of Grasp and Individuated Finger Movements Based on Low-Density Myoelectric Signals es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.riai.2017.02.001
dc.relation.projectID info:eu-repo/grantAgreement/CAPES//FAPES%2F007%2F2014/ es_ES
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Villarejo Mayor, JJ.; Mamede Costa, R.; Frizera Neto, A.; Freire Bastos, T. (2017). Decodificación de Movimientos Individuales de los Dedos y Agarre a Partir de Señales Mioeléctricas de Baja Densidad. Revista Iberoamericana de Automática e Informática industrial. 14(2):184-192. https://doi.org/10.1016/j.riai.2017.02.001 es_ES
dc.description.accrualMethod OJS es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.riai.2017.02.001 es_ES
dc.description.upvformatpinicio 184 es_ES
dc.description.upvformatpfin 192 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 14 es_ES
dc.description.issue 2 es_ES
dc.identifier.eissn 1697-7920
dc.relation.pasarela OJS\9223 es_ES
dc.contributor.funder Coordenaçao de Aperfeiçoamento de Pessoal de Nível Superior, Brasil es_ES
dc.contributor.funder Fundação de Amparo à Pesquisa e Inovação do Espírito Santo, Brasil es_ES
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